6

我有一个相当棘手的问题，我似乎无法解决。

我有一个大型数据集（23277 行，151 列）。每列都有从 0:100（含）开始的值，表示为世界上的事件分配的概率。

作为计算每个人分数的一部分，我需要计算数据集中每个值的出现次数。

我第一次尝试应用，但我需要忽略 NA 和子集，所以当我尝试以下操作时：

apply(ans.samp, 1, sum(ans.samp[ans==0]), na.rm=TRUE)

我收到错误消息： sum(ans.samp[ans == 0])' 不是函数、字符或符号

我用 sapply、vapply、tapply 和 do.call 重复了这个过程，但无济于事。

放弃矢量化解决方案，我编写了以下 for 循环。

RespCount <- function (x) { for (i in (1:nrow(x))) 
  { res <- vector(mode="numeric", length=nrow(x))
    ans.tmp <- x[i,]
    res[i] <- length(ans.tmp[ans.tmp==0])
    print(res)
  }
return(res)
}

然而，在我得到这个工作之后，它只返回样本中 O 的总和。

我会很感激这方面的帮助，因为我有一些时间压力，我希望将来能够在 R 中解决这些类型的问题。

包括用于重现性的样本数据：

structure(list(X = 1:6, X100 = c(70L, NA, 80L, 0L, 40L, NA), 
    X10 = c(30L, NA, NA, NA, NA, NA), X1 = c(50L, NA, NA, NA, 
    NA, NA), X11 = c(50L, NA, NA, NA, NA, NA), X12 = c(30L, NA, 
    NA, NA, NA, NA), X13 = c(50L, NA, NA, NA, NA, NA), X14 = c(70L, 
    NA, NA, NA, NA, NA), X15 = c(60L, NA, NA, NA, NA, NA), X158 = c(30L, 
    NA, NA, NA, NA, NA), X159 = c(50L, NA, NA, NA, NA, NA), X160 = c(80L, 
    NA, NA, NA, NA, NA), X16 = c(50L, NA, NA, NA, NA, NA), X161 = c(40L, 
    NA, NA, NA, NA, NA), X162 = c(100L, NA, NA, NA, NA, NA), 
    X163 = c(50L, NA, NA, NA, NA, NA), X164 = c(0L, NA, NA, NA, 
    NA, NA), X165 = c(0L, NA, NA, NA, NA, NA), X166 = c(20L, 
    NA, NA, NA, NA, NA), X167 = c(0L, NA, NA, NA, NA, NA), X168 = c(30L, 
    NA, NA, NA, NA, NA), X169 = c(100L, NA, NA, NA, NA, NA), 
    X170 = c(30L, NA, NA, NA, NA, NA), X17 = c(40L, NA, NA, NA, 
    NA, NA), X171 = c(50L, NA, NA, NA, NA, NA), X172 = c(20L, 
    NA, NA, NA, NA, NA), X173 = c(30L, NA, NA, NA, NA, NA), X174 = c(20L, 
    NA, NA, NA, NA, NA), X175 = c(30L, NA, NA, NA, NA, NA), X176 = c(10L, 
    NA, NA, NA, NA, NA), X177 = c(70L, NA, NA, NA, NA, NA), X178 = c(40L, 
    NA, NA, NA, NA, NA), X179 = c(70L, NA, NA, NA, NA, NA), X180 = c(0L, 
    NA, NA, NA, NA, NA), X18 = c(30L, NA, NA, NA, NA, NA), X181 = c(100L, 
    NA, NA, NA, NA, NA), X182 = c(100L, NA, NA, NA, NA, NA), 
    X183 = c(20L, NA, NA, NA, NA, NA), X184 = c(80L, NA, NA, 
    NA, NA, NA), X185 = c(90L, NA, NA, NA, NA, NA), X186 = c(0L, 
    NA, NA, NA, NA, NA), X187 = c(10L, NA, NA, NA, NA, NA), X188 = c(100L, 
    NA, NA, NA, NA, NA), X189 = c(100L, NA, NA, NA, NA, NA), 
    X190 = c(0L, NA, NA, NA, NA, NA), X19 = c(100L, NA, NA, NA, 
    NA, NA), X191 = c(0L, NA, NA, NA, NA, NA), X192 = c(90L, 
    NA, NA, NA, NA, NA), X193 = c(50L, NA, NA, NA, NA, NA), X194 = c(100L, 
    NA, NA, NA, NA, NA), X195 = c(10L, NA, NA, NA, NA, NA), X196 = c(100L, 
    NA, NA, NA, NA, NA), X197 = c(20L, NA, NA, NA, NA, NA), X198 = c(40L, 
    NA, NA, NA, NA, NA), X199 = c(20L, NA, NA, NA, NA, NA), X200 = c(0L, 
    NA, NA, NA, NA, NA), X20 = c(0L, NA, NA, NA, NA, NA), X201 = c(0L, 
    NA, NA, NA, NA, NA), X202 = c(20L, NA, NA, NA, NA, NA), X203 = c(20L, 
    NA, NA, NA, NA, NA), X204 = c(80L, NA, NA, NA, NA, NA), X205 = c(0L, 
    NA, NA, NA, NA, NA), X206 = c(80L, NA, NA, NA, NA, NA), X207 = c(0L, 
    NA, NA, NA, NA, NA), X2 = c(10L, NA, NA, NA, NA, NA), X21 = c(0L, 
    NA, NA, NA, NA, NA), X22 = c(100L, NA, NA, NA, NA, NA), X23 = c(50L, 
    NA, NA, NA, NA, NA), X24 = c(50L, NA, NA, NA, NA, NA), X25 = c(70L, 
    NA, NA, NA, NA, NA), X26 = c(60L, NA, NA, NA, NA, NA), X27 = c(40L, 
    NA, NA, NA, NA, NA), X28 = c(20L, NA, NA, NA, NA, NA), X29 = c(0L, 
    NA, NA, NA, NA, NA), X30 = c(90L, NA, NA, NA, NA, NA), X3 = c(0L, 
    NA, NA, NA, NA, NA), X31 = c(50L, NA, NA, NA, NA, NA), X32 = c(50L, 
    NA, NA, NA, NA, NA), X33 = c(0L, NA, NA, NA, NA, NA), X34 = c(50L, 
    NA, NA, NA, NA, NA), X35 = c(90L, NA, NA, NA, NA, NA), X36 = c(50L, 
    NA, NA, NA, NA, NA), X37 = c(60L, NA, NA, NA, NA, NA), X38 = c(40L, 
    NA, NA, NA, NA, NA), X39 = c(50L, NA, NA, NA, NA, NA), X40 = c(0L, 
    NA, NA, NA, NA, NA), X4 = c(50L, NA, NA, NA, NA, NA), X41 = c(90L, 
    NA, NA, NA, NA, NA), X42 = c(80L, NA, NA, NA, NA, NA), X43 = c(50L, 
    NA, NA, NA, NA, NA), X44 = c(80L, NA, NA, NA, NA, NA), X45 = c(80L, 
    NA, NA, NA, NA, NA), X46 = c(0L, NA, NA, NA, NA, NA), X47 = c(80L, 
    NA, NA, NA, NA, NA), X48 = c(20L, NA, NA, NA, NA, NA), X49 = c(100L, 
    NA, NA, NA, NA, NA), X50 = c(0L, NA, NA, NA, NA, NA), X5 = c(0L, 
    NA, NA, NA, NA, NA), X51 = c(80L, 100L, 70L, 100L, 0L, 60L
    ), X52 = c(10L, 0L, 0L, 0L, 0L, 20L), X53 = c(40L, 40L, 70L, 
    20L, 90L, 50L), X54 = c(0L, 10L, 0L, 50L, 50L, 0L), X55 = c(20L, 
    80L, 90L, 80L, 30L, 0L), X56 = c(100L, 100L, 50L, 100L, 80L, 
    100L), X57 = c(60L, 0L, 100L, 70L, 100L, 80L), X58 = c(100L, 
    100L, 100L, 50L, 100L, 100L), X59 = c(80L, 50L, 80L, 0L, 
    30L, 50L), X60 = c(70L, 50L, 60L, 50L, 100L, 100L), X6 = c(100L, 
    NA, NA, NA, NA, NA), X61 = c(50L, 50L, 50L, 30L, 70L, 50L
    ), X62 = c(20L, 50L, 40L, 40L, 50L, 100L), X63 = c(50L, 0L, 
    100L, 10L, 50L, 100L), X64 = c(60L, 30L, 0L, 50L, 50L, 50L
    ), X65 = c(50L, 50L, 70L, 80L, 50L, 50L), X66 = c(70L, 40L, 
    10L, 90L, 60L, 50L), X67 = c(30L, 50L, 50L, 0L, 50L, 60L), 
    X68 = c(30L, 0L, 0L, 40L, 70L, 80L), X69 = c(30L, NA, 70L, 
    10L, 0L, 20L), X70 = c(80L, NA, 50L, 50L, 70L, 100L), X7 = c(100L, 
    NA, NA, NA, NA, NA), X71 = c(70L, NA, 50L, 100L, 100L, 100L
    ), X72 = c(60L, NA, 70L, 50L, 80L, 50L), X73 = c(80L, NA, 
    80L, 80L, 80L, NA), X74 = c(50L, NA, 50L, 0L, 50L, NA), X75 = c(30L, 
    NA, 70L, 10L, 80L, NA), X76 = c(70L, NA, 40L, 80L, 100L, 
    NA), X77 = c(80L, NA, 50L, 100L, 40L, NA), X78 = c(80L, NA, 
    0L, 0L, 0L, NA), X79 = c(80L, NA, 50L, 50L, 50L, NA), X80 = c(40L, 
    NA, 90L, 70L, 60L, NA), X8 = c(50L, NA, NA, NA, NA, NA), 
    X81 = c(70L, NA, 60L, 40L, 80L, NA), X82 = c(80L, NA, 100L, 
    60L, 60L, NA), X83 = c(30L, NA, 100L, 30L, 0L, NA), X84 = c(80L, 
    NA, 0L, 60L, 100L, NA), X85 = c(80L, NA, 50L, 40L, 30L, NA
    ), X86 = c(50L, NA, 90L, 50L, 50L, NA), X87 = c(80L, NA, 
    50L, 70L, 20L, NA), X88 = c(40L, NA, 70L, 30L, 90L, NA), 
    X89 = c(50L, NA, 50L, 80L, 80L, NA), X90 = c(90L, NA, 100L, 
    60L, 100L, NA), X91 = c(0L, NA, 0L, 0L, 0L, NA), X9 = c(100L, 
    NA, NA, NA, NA, NA), X92 = c(50L, NA, 70L, 90L, 80L, NA), 
    X93 = c(40L, NA, 50L, 50L, 50L, NA), X94 = c(40L, NA, 0L, 
    60L, 40L, NA), X95 = c(90L, NA, 100L, 40L, 50L, NA), X96 = c(50L, 
    NA, 50L, 50L, 50L, NA), X97 = c(60L, NA, 60L, 100L, 50L, 
    NA), X98 = c(40L, NA, 40L, 0L, 0L, NA), X99 = c(30L, NA, 
    0L, 50L, 70L, NA)), .Names = c("X", "X100", "X10", "X1", 
"X11", "X12", "X13", "X14", "X15", "X158", "X159", "X160", "X16", 
"X161", "X162", "X163", "X164", "X165", "X166", "X167", "X168", 
"X169", "X170", "X17", "X171", "X172", "X173", "X174", "X175", 
"X176", "X177", "X178", "X179", "X180", "X18", "X181", "X182", 
"X183", "X184", "X185", "X186", "X187", "X188", "X189", "X190", 
"X19", "X191", "X192", "X193", "X194", "X195", "X196", "X197", 
"X198", "X199", "X200", "X20", "X201", "X202", "X203", "X204", 
"X205", "X206", "X207", "X2", "X21", "X22", "X23", "X24", "X25", 
"X26", "X27", "X28", "X29", "X30", "X3", "X31", "X32", "X33", 
"X34", "X35", "X36", "X37", "X38", "X39", "X40", "X4", "X41", 
"X42", "X43", "X44", "X45", "X46", "X47", "X48", "X49", "X50", 
"X5", "X51", "X52", "X53", "X54", "X55", "X56", "X57", "X58", 
"X59", "X60", "X6", "X61", "X62", "X63", "X64", "X65", "X66", 
"X67", "X68", "X69", "X70", "X7", "X71", "X72", "X73", "X74", 
"X75", "X76", "X77", "X78", "X79", "X80", "X8", "X81", "X82", 
"X83", "X84", "X85", "X86", "X87", "X88", "X89", "X90", "X91", 
"X9", "X92", "X93", "X94", "X95", "X96", "X97", "X98", "X99"), row.names = c(NA, 
6L), class = "data.frame")

任何见解将不胜感激。

从上面对小数据集的一些尝试来看，似乎正在为每一行计算数字，但是当我返回 res 对象时，它只是给了我最终值。我怎样才能解决这个问题？

4

4 回答 4

14

有两种方法可以使用apply家庭功能。要么你做

apply(mat, 1, sum, na.rm=TRUE)

如果要将函数sum()应用于每一行，请传递其他参数，例如na.rm=TRUE. 或者你可以做

apply(mat, 1, foo)

哪里foo()是您自己的函数，在外部定义，例如

foo <- function(x) sum(x==0, na.rm=TRUE)

请注意，NA 处理也可能处理函数本身的参数，默认值设置为TRUE，在上面的定义中，如

foo2 <- function(x, no.na=TRUE) sum(x==0, na.rm=no.na)

你可以调用它，apply(mat, 1, foo2, no.na=F)尽管它对函数没有意义 sum()（除非你想检查是否有 NA 值，但在这种情况下最好使用is.na():-)。

最后，您可以将函数直接内联定义为

apply(mat, 1, function(x) sum(x==0, na.rm=TRUE))

在你的情况下，它给了我

> apply(mat, 1, function(x) sum(x==0, na.rm=TRUE))
 1  2  3  4  5  6 
22  4  9  8  7  2

这相当于apply(ex, 1, foo)。

于 2011-05-15T19:46:59.787 回答
4

让我们调用您的数据集dat。您可以使用table()获取数据集中每个值的频率表。如果要将其应用于数据框中的所有数据，请将数据强制转换为单个向量，并table()在结果向量上使用：

table(do.call('c', dat))

这给了你：

> table(do.call('c', dat))
  0   1   2   3   4   5   6  10  20  30  40  50  60  70  80  90 100 
 52   1   1   1   1   1   1  10  16  21  25  76  19  25  37  14  45

如果要检查各个列的频率，只需执行以下操作：

apply(dat, 1, table)
于 2011-05-15T20:22:04.647 回答
4

对于名为 的 data.frame 中的数据df，

sapply(df + 1, tabulate, 101)

生成一个 101 x 151 的矩阵，其中行对应于 0、1、...、100，列对应于 151 个样本；矩阵可能便于后续计算，而制表比表快。

于 2011-05-15T23:35:45.647 回答
3

我试图解决问题陈述，而不是在最初的部分努力中纠正编码问题。要计算连续出现的次数，请使用 'apply' 和 'table'

> apply(dfrm, 1, table)
$`1`

  0   1  10  20  30  40  50  60  70  80  90 100 
 22   1   5  12  14  12  26   7  10  19   7  16 

$`2`

  0   2  10  30  40  50  80 100 
  4   1   1   1   2   6   1   3 

$`3`

  0   3  10  40  50  60  70  80  90 100 
  9   1   1   3  13   3   8   3   3   7 

$`4`

  0   4  10  20  30  40  50  60  70  80  90 100 
  8   1   3   1   3   5  11   4   3   5   2   5 

$`5`

  0   5  20  30  40  50  60  70  80  90 100 
  7   1   1   3   3  13   3   4   7   2   7 

$`6`

  0   6  20  50  60  80 100 
  2   1   2   7   2   2   7

请注意，此结果包括 x==0 情况的子集：

> sapply( apply(dfrm, 1, table), function(x) x['0'])
1.0 2.0 3.0 4.0 5.0 6.0 
 22   4   9   8   7   2
于 2011-05-15T21:43:16.397 回答